Autonomous Analyst

🧠 Overview

Autonomous Analyst is a local, agentic AI pipeline that:

• Analyzes tabular data

• Detects anomalies with Mahalanobis distance

• Uses a local LLM (llama3.2:1b via Ollama) to generate interpretive summaries

• Logs results to ChromaDB for semantic recall

• Is fully orchestrated via the Model Context Protocol (MCP)

⚙️ Features

🧪 Tools Defined (via MCP)

🤖 Agentic Capabilities

• Autonomy: LLM-guided execution path selection with autonomous_plan

• Tool Use: Dynamically invokes registered MCP tools via LLM inference

• Reasoning: Generates technical insights from dataset conditions and outlier analysis

• Memory: Persists and recalls knowledge using ChromaDB vector search

• LLM: Powered by Ollama with llama3.2:1b (temperature = 0.1, deterministic)

Related MCP server: coremcp

🚀 Getting Started

1. Clone and Set Up

git clone https://github.com/MadMando/mcp-autonomous-analyst.git
cd mcp-autonomous-analyst
conda create -n mcp-agentic python=3.11 -y
conda activate mcp-agentic
pip install uv
uv pip install -r requirements.txt

2. Start the MCP Server

mcp run server.py --transport streamable-http

3. Start the Web Dashboard

uvicorn web:app --reload --port 8001

Then visit: http://localhost:8000

🌐 Dashboard Flow

• Step 1: Upload your own dataset or click Generate Synthetic Data

• Step 2: The system runs anomaly detection on feature_1 vs feature_2

• Step 3: Visual plot of outliers is generated

• Step 4: Summaries are created via LLM

• Step 5: Results are optionally logged to vector store for recall

📁 Project Layout

📦 autonomous-analyst/
├── server.py                  # MCP server
├── web.py                     # FastAPI + MCP client (frontend logic)
├── tools/
│   ├── synthetic_data.py
│   ├── outlier_detection.py
│   ├── plotter.py
│   ├── summarizer.py
│   ├── vector_store.py
├── static/                   # Saved plot
├── data/                     # Uploaded or generated dataset
├── requirements.txt
├── .gitignore
└── README.md

📚 Tech Stack

• MCP SDK: mcp

• LLM Inference: Ollama running llama3.2:1b

• UI Server: FastAPI + Uvicorn

• Memory: ChromaDB vector database

• Data: pandas, matplotlib, scikit-learn

✅ .gitignore Additions

__pycache__/
*.pyc
*.pkl
.env
static/
data/

🙌 Acknowledgements

This project wouldn't be possible without the incredible work of the open-source community. Special thanks to:

💡 If you use this project, please consider starring or contributing to the upstream tools that make it possible.

This repo was created with the assistance of a local rag-llm using llama3.2:1b